Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Logic Programming in Assumption-Based Argumentation Revisited – Semantics and Graphical Representation
Authors: Claudia Schulz, Francesca Toni
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We revisit this initial work by proving that the 3-valued stable semantics of a logic program coincides with the complete semantics of the encoding ABA framework, and that the L-stable semantics of this logic program coincides with the semi-stable semantics of the encoding ABA framework. Furthermore, we show how to graphically represent the structure of a logic program encoded in an ABA framework and that not only logic programming and ABA semantics but also Abstract Argumentation semantics can be easily applied to a logic program using these graphical representations. |
| Researcher Affiliation | Academia | Claudia Schulz and Francesca Toni EMAIL Department of Computing Imperial College London London SW7 2AZ, UK |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code, nor does it explicitly state that code for the methodology described is released. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not perform experiments requiring specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific ancillary software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details, hyperparameters, or training configurations. |